Zahraa Abdallah is a Senior Lecturer at the School of Engineering Mathematics and Technology, University of Bristol. She holds a PhD and BSc in relevant fields. Her research focuses on Machine Learning, Data Science, Time Series Analysis, and their applications in Health Informatics, Neuroscience, and Bioinformatics. She leads projects on wearable technology integration for diabetes management and EEG-based disease classification. Her work emphasizes explainable AI and multimodal approaches. Zahraa is affiliated with the Bristol Doctoral College Initiative (BDFI) as an Academic Co-Director and collaborates with experts like Prof. Raul Santos-Rodriguez. Contact: zahraa.abdallah@bristol.ac.uk | Website: zahraa-abdallah.com Research Interests: Time Series Clustering & Forecasting EEG-based Disease Detection (Parkinson’s, Alzheimer’s) Smartwatch-Driven Healthcare Systems Explainable AI in Biomedical Applications Key Projects: Development of the CSTS benchmark for time series clustering Investigating insulin needs using automated delivery data Gene essentiality classification via graph neural networks Collaborations: Professor Raul Santos-Rodriguez (BDFI) Lucia Marucci (Systems & Engineering Biology)
Michael A. Newton is a Professor and Chair of the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison, School of Medicine and Public Health. His research focuses on statistical methodologies for high-dimensional biomedical data, including cancer biology, immunology, and genomics. He is renowned for developing empirical Bayesian methods, stochastic models, and computational tools for analyzing molecular data. His work integrates statistical theory with interdisciplinary collaborations, contributing to advancements in translational biomedicine. Newton has held prestigious awards, including the Mortimer Spiegelman Award (2003) and the COPSS Presidents' Award (2004). He is an elected Fellow of the American Statistical Association and an elected Member of the International Statistical Institute. He leads the Biostatistics and Epidemiology Research and Design (BERD) core at the Institute for Clinical and Translational Research and is affiliated with the Carbone Comprehensive Cancer Center and the Center for Genome Science and Innovation. His teaching includes advanced courses in computational statistics, Bayesian analysis, and statistical methods in molecular biology. Newton directs graduate programs in Statistics and Biomedical Data Science, emphasizing interdisciplinary training.
Dr. Zhenghao Chen is an Assistant Professor at the University of Newcastle. He holds a B.Eng. H1 and Ph.D. from the University of Sydney (2017 and 2022). His research focuses on Computer Vision, NLP, and Machine Learning, with expertise in Generative AI. He has published in top conferences like CVPR and journals such as IEEE T-IP. Awards include the Google Australia Prize and ACM SIGMM Outstanding Thesis Award. He previously worked at TikTok and Disney Research, and serves on program committees for major conferences. Research interests emphasize generative models and industrial applications. His publications span topics like image compression, facial recognition, and medical imaging. Awards highlight academic and industrial recognition. Teaching includes courses on visual signal understanding and video intelligence. Current roles include HDR recruitment and organizing international workshops.
Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.
Karim ZKIK is an Associate Professor of Cyber Security and Information Systems at ESAIP Graduate School of Engineering, Angers, France. Previously, he served as an Assistant Professor at the International University of Rabat (UIR), Morocco. His roles include Educational Manager of the Cyber Security track, Head of the Cybersecurity Innovation Hub, and committee member for ABET certification and curriculum design. He actively contributes to academic service, organizing conferences such as the International Conference on Cryptology, Coding Theory, and Cyber Security (I4CS 2022), and serves as a Guest Editor for Computers and Industrial Engineering . His research focuses on cybersecurity for connected systems, blockchain technologies, AI-driven security solutions, and cyber resilience in industrial control systems. Recent work explores integrating blockchain and machine learning for threat detection, secure IoT networks, and supply chain resilience. Key contributions include frameworks for cyber resilience in retail and airlines, blockchain-based crowdfunding security, and SDN-based attack mitigation. ZKIK holds a Habilitation (2024) and PhD in Cyber Security from Université d’Angers and Mohamed V University, Rabat. He holds over 20 certifications from EC-Council, IBM, and Cisco. His work bridges theoretical research and industry applications, addressing challenges in smart environments, industrial systems, and sustainable supply chains.
Yannis Kevrekidis is a Professor at Princeton University with a distinguished career in computational mathematics and chemical engineering. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS) and has held visiting positions at institutions like the Zuse Institute Berlin and Caltech. Education : National Technical University of Athens (Chemical Engineering) University of Minnesota (PhD in dynamical systems) Research Interests : Equation-Free and Variable-Free Modeling Complex Systems Dynamics Multiscale Computation Integration of Machine Learning with Scientific Computing Pattern Formation & Instability Analysis Key Article Trends : Advanced data-driven modeling of dynamical systems Manifold learning for reaction coordinates Projective integration methods Coarse-grained modeling across disciplines Applications in epidemiology, neuroscience, and fluid dynamics Scientific Awards : Guggenheim Fellowship Humboldt Research Award Computing in Chemical Engineering Award (AIChE) Bodossaki Academic Award Allan P. Colburn Award Collaborations : Extensive international collaborations with institutions in Germany, Austria, and the UK Key role in the Complex Systems Modeling and Computation focus group at TUM-IAS
Marina L. Gavrilova is a Professor at the University of Calgary, Canada. Her research focuses on biometric systems, computer vision, and machine learning with an emphasis on multimodal recognition and security applications. She has authored numerous publications in top journals and conferences, contributing to advancements in fields like emotion-aware de-identification, generative adversarial networks, and ethical AI frameworks in healthcare. Her work spans social behavioral biometrics, gait recognition, masked face recognition, and aesthetic-based person identification. Key contributions include frameworks for ethical AI in care systems, fusion algorithms for multi-biometric systems, and innovations in visual and audio signal processing. Collaborations with experts like Osvaldo Gervasi, Jon G. Rokne, and Padma Polash Paul highlight her interdisciplinary approach. Publications emphasize practical applications such as privacy-preserved biometrics, emotion detection from social media, and adaptive systems for template aging. Despite no explicit mention of grants or labs, her extensive co-author network and frequent citations indicate significant academic influence.
Dr. Swati Chandna is a Senior Lecturer at the School of Computing and Mathematical Sciences, Birkbeck, University of London. She holds an honorary position as an Honorary Lecturer in Statistics at University College London (UCL) from January 2023 to January 2026. She earned her PhD in Statistics from Imperial College London in 2013. Her research focuses on statistical modeling, network analysis, and bioinformatics, with notable contributions to stochastic networks, single-cell genomic data analysis, and complex-valued signal processing. Teaching responsibilities include modules such as Bayesian Methods, Analysing Data, Statistical Analysis, and Project Applied Statistics. She serves as Admissions Tutor for Graduate Certificate and Diploma in Statistics for Data Science and as School Ethics Lead at Birkbeck. Her work bridges theoretical statistics with practical applications in genomics, environmental modeling, and biomedical research. Dr. Chandna’s recent research explores topics like covariate-driven network estimation, stochastic modeling of genomic data, and bootstrap techniques in source separation. Her publications reflect interdisciplinary collaboration across statistics, computer science, and life sciences.
Mark Kramer is a Professor in the Department of Mathematics & Statistics at Boston University. He belongs to the Applied Mathematics research group, focusing on mathematical, statistical, and machine learning approaches to characterize brain activity. His work bridges data-driven neuroscience with computational methods, exploring topics like biophysical models of neurons, field models of neural populations in epilepsy, and theoretical questions about brain rhythms. His research interests include: Biophysical modeling of single-neuron dynamics Neural population activity in pathological states Machine learning for detecting abnormal brain rhythms Analysis of cross-frequency coupling and coherence Kramer has developed educational resources like Case Studies in Neural Data Analysis using both MATLAB and Python. These materials teach practical data analysis techniques for spike trains and field data, emphasizing hands-on implementation over theoretical mathematics. He has received funding from NIH and NSF for computational neuroscience projects. His recent publications focus on epilepsy research, sleep spindle analysis, and neural signal processing. The work spans from developing statistical frameworks to understanding network dynamics in seizure termination and exploring phase consistency in neural data. Notably, his coherence studies revealed non-intuitive coupling patterns between brain regions, demonstrating that low-amplitude rhythms can be more informative than dominant ones.
Professor Dong Xu is a Tenured Professor in the Department of Computer Science at the University of Hong Kong (HKU), part of the School of Computing and Data Science. He holds a B.Eng. and Ph.D. from the University of Science and Technology of China (USTC). His career includes tenured roles at Nanyang Technological University and the University of Sydney, alongside postdoctoral research at Columbia University. His research focuses on Artificial Intelligence, Computer Vision, Multimedia, and Machine Learning , with applications in autonomous driving, AR/VR, medical image analysis, and video surveillance. Xu has authored over 150 papers in top journals and conferences, including CVPR, ICCV, and IEEE Transactions. He actively contributes to the academic community as an editorial board member for journals like ACM Computing Surveys and IEEE Transactions, and through leadership roles in conferences such as ACM Multimedia and ICME. Notable awards include Fellowships from IEEE and IAPR, and the IEEE Signal Processing Society Distinguished Lecturer title (2021–2022). Education: B.Eng. (USTC, 2001), Ph.D. (USTC, 2005) Professional Service: Program Coordinator of ACM Multimedia 2024, Guest Editor of over ten special issues.
David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.
Chen Binbin is an Associate Professor and Associate Head of Pillar (Innovation and Enterprise) in the Information Systems Technology and Design (ISTD) pillar at Singapore University of Technology and Design (SUTD). He serves as Deputy Director for the Future Communications Research and Development Programme (FCP), Singapore. Previously, he was a Principal Research Scientist at the Advanced Digital Sciences Center (now Illinois ARCS), affiliated with the University of Illinois. Education: PhD in Computer Science from National University of Singapore, and Bachelor's from Peking University. Research focuses on wireless networking, distributed systems, and cyber security for critical infrastructures like smart grids and industrial control systems. His work addresses secure communications, intrusion detection, and resilience against cyber-physical threats. Notable contributions include error-estimating coding, provenance verification in ICS, and AI-driven network security solutions. Key awards include the 2010 ACM SIGCOMM Best Paper Award for error-estimating coding research. His grants span agencies like Singapore's National Research Foundation (NRF), Cyber Security Agency (CSA), and Energy Market Authority (EMA). He leads projects on secure smart grid communication, industrial control system defense, and AI-enhanced cybersecurity tools. Technical leadership involves developing frameworks like CyberSAGE for security assessment and CMD for IoT malware detection. Active in collaborations with industry and government, his work bridges theory and practice in securing critical infrastructure systems.
Prof. Dr. Beate Escher is Head of the Department of Cell Toxicology at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. She holds professorial positions at Eberhard Karls University of Tübingen , is a Privatdozent at ETH Zurich , and is affiliated with the University of Queensland and Griffith University in Australia. Her research program focuses on advancing in vitro bioassays and New Approach Methods (NAMs) for environmental and human health risk assessment of micropollutants. Her research interests lie at the intersection of environmental toxicology , molecular toxicology , and exposure science . She develops and applies bioanalytical tools for water quality assessment, with a focus on pharmaceuticals, pesticides, and transformation products. Her work includes mechanism-based toxicity assessment , toxicokinetic-toxicodynamic (TKTD) modeling , and the development of the CITEPro robotic bioassay platform for high-throughput screening. She integrates omics data , computational modeling , and machine learning to improve chemical hazard characterization. Recent publications highlight trends in chemical mixture toxicity , safe-by-design chemicals , ionic compound assessment , and machine learning applications in toxicology. Her work increasingly leverages data-driven approaches to prioritize contaminants and predict biological effects across species. Scientific Awards: Highly Cited Researcher (Web of Science/Clarivate, Top 0.1%, 2020) Outstanding Achievements in Environmental Science and Technology (ES&T & ACS ENVR, 2023) Advising and Grants: She supervises multiple doctoral students and leads major collaborative projects such as InCeTo, MibiTox, nanoINHALE, and SafePol. She received an Australian Research Council grant (2011–2014) and leads Swiss National Science Foundation-funded initiatives. She was a member of the German Science Council (2017–2024) and serves on the Board of Reviewing Editors of SCIENCE . Labs and Teams: She leads the Cell Toxicology team at UFZ, which includes researchers such as Dr. Luise Henneberger, Dr. Julia Huchthausen, and Dr. Haotian Wang. The team operates the CITEPro platform and contributes to international consortia focused on exposome research and chemical safety.
Steven Halim is an Associate Professor (Educator Track) in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He has been a full-time educator since 2007, teaching a wide range of courses including Data Structures and Algorithms, Competitive Programming, and Design and Analysis of Algorithms. He currently serves as the Director of the Centre for Nurturing Computing Excellence (CeNCE) and is a Fellow of the NUS Teaching Academy. Ph.D. in Computer Science, National University of Singapore B.Sc. in Computer Science, National University of Singapore His research and teaching interests focus on algorithms, competitive programming, visualization, and optimization. He is renowned for creating VisuAlgo , an interactive online platform that visualizes data structures and algorithms, used by students and educators worldwide. He is also the co-author of the widely acclaimed book "Competitive Programming" , now in its fourth edition, which is a key resource for programming contest preparation. His recent scholarly work centers on pedagogical innovations in computer science education, particularly algorithm visualization and competitive programming methodologies. Earlier publications from his PhD work focused on stochastic local search, metaheuristics, and algorithm tuning through visualization. The articles span topics from educational technology to combinatorial optimization, reflecting a transition from research in algorithm engineering to leadership in computing education. Commendation Medal (Pingat Kepujian), National Day Awards 2018 NUS Annual Teaching Excellence Award (ATEA) 2014/15, 2017/18, 2018/19 (with Honour Roll) Faculty Teaching Excellence Award (FTEA) 2011/12, 2012/13, 2014/15 (with Honour Roll) Best Teaching Assistant Award 2007/08 Steven Halim has advised numerous students through his courses and competitive programming teams. He has served as the head coach for NUS ICPC teams since 2008 and team leader for Singapore IOI teams since 2009, leading them to multiple international medals. He has also held leadership roles in major international competitions, including Deputy Director for IOI 2020 and 2021, and Regional Contest Director for ICPC Asia Singapore 2015 and 2018. He was a Resident Fellow at NUS Sheares Hall for nine years, deeply engaging with student life. He leads the Centre for Nurturing Computing Excellence (CeNCE), where he manages programming competition activities for both NUS and Singapore national teams. His work integrates education, competition, and mentorship, creating a synergistic environment that has significantly elevated Singapore's performance in international informatics olympiads.
Naoki Saito is a Professor in the Department of Mathematics at the University of California, Davis, and the Director of the UC Davis TETRAPODS Institute of Data Science (UCD4IDS). His research lies at the intersection of applied mathematics, signal processing, and data science, with a focus on multiscale analysis and harmonic analysis on graphs and networks. His research interests include Applied and Computational Harmonic Analysis , Graph Signal Processing , Multiscale Transforms , Wavelets , Spectral Graph Theory , and Mathematical Data Representation . He develops theoretical frameworks and practical algorithms for analyzing complex datasets, particularly through the use of Laplacian eigenfunctions and multiscale basis dictionaries. The recent publications reflect a strong trend toward graph-based signal processing , scattering transforms , and topological data analysis . His work emphasizes the construction of natural, adaptive bases for signals on graphs and simplicial complexes, enabling efficient and interpretable data analysis. The integration of harmonic analysis with machine learning techniques is a recurring theme. Although no specific scientific awards are listed in the provided texts, his sustained scholarly output and leadership in the field are evident. Dr. Saito advises a number of students and postdoctoral researchers, including J. Irion, Y. Shao, H. Li, and others. His research has been supported by various grants, though specific funding sources are not detailed in the provided materials. He leads the UCD4IDS, a research institute focused on data science, indicating active involvement in collaborative, interdisciplinary research and academic leadership.
